Grab lab file using command line:
# Step 1
cd ~/Documents
mkdir lab11
cd lab11
# Step 2
wget https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/11-lab.Rmd
And remember to set eval=TRUE
plot_ly() and ggplotly() functionsplot_geo()We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.
The objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this
## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data
## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data
### FINISH THE CODE HERE ###
# load COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))
### FINISH THE CODE HERE ###
# load state population data
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL
### FINISH THE CODE HERE
cv_states <- merge(cv_states, state_pops, by="state")
head, and tail of
the datadim(cv_states)
## [1] 58094 9
head(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 1 Alabama 2023-01-04 1 1587224 21263 1 4887871 96.50939 AL
## 2 Alabama 2020-04-25 1 6213 213 1 4887871 96.50939 AL
## 3 Alabama 2023-02-26 1 1638348 21400 1 4887871 96.50939 AL
## 4 Alabama 2022-12-03 1 1549285 21129 1 4887871 96.50939 AL
## 5 Alabama 2020-05-06 1 8691 343 1 4887871 96.50939 AL
## 6 Alabama 2021-04-21 1 524367 10807 1 4887871 96.50939 AL
tail(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 58089 Wyoming 2022-09-11 56 175290 1884 56 577737 5.950611 WY
## 58090 Wyoming 2022-08-21 56 173487 1871 56 577737 5.950611 WY
## 58091 Wyoming 2021-01-26 56 51152 596 56 577737 5.950611 WY
## 58092 Wyoming 2021-02-21 56 53795 662 56 577737 5.950611 WY
## 58093 Wyoming 2021-08-22 56 70671 809 56 577737 5.950611 WY
## 58094 Wyoming 2021-03-20 56 55581 693 56 577737 5.950611 WY
str(cv_states)
## 'data.frame': 58094 obs. of 9 variables:
## $ state : chr "Alabama" "Alabama" "Alabama" "Alabama" ...
## $ date : IDate, format: "2023-01-04" "2020-04-25" ...
## $ fips : int 1 1 1 1 1 1 1 1 1 1 ...
## $ cases : int 1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ...
## $ deaths : int 21263 213 21400 21129 343 10807 19676 16756 16826 15179 ...
## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ...
## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ...
## $ abb : chr "AL" "AL" "AL" "AL" ...
# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")
# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)
### FINISH THE CODE HERE
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]
# Confirm the variables are now correctly formatted
str(cv_states)
## 'data.frame': 58094 obs. of 9 variables:
## $ state : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ date : Date, format: "2020-03-13" "2020-03-14" ...
## $ fips : int 1 1 1 1 1 1 1 1 1 1 ...
## $ cases : int 6 12 23 29 39 51 78 106 131 157 ...
## $ deaths : int 0 0 0 0 0 0 0 0 0 0 ...
## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ...
## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ...
## $ abb : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
head(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
## 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
## 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
## 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
## 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
## 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
tail(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 57902 Wyoming 2023-03-18 56 185640 2009 56 577737 5.950611 WY
## 57916 Wyoming 2023-03-19 56 185640 2009 56 577737 5.950611 WY
## 57647 Wyoming 2023-03-20 56 185640 2009 56 577737 5.950611 WY
## 57867 Wyoming 2023-03-21 56 185800 2014 56 577737 5.950611 WY
## 58057 Wyoming 2023-03-22 56 185800 2014 56 577737 5.950611 WY
## 57812 Wyoming 2023-03-23 56 185800 2014 56 577737 5.950611 WY
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)
## state date fips cases deaths geo_id population pop_density abb
## 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
## 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
## 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
## 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
## 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
## 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
summary(cv_states)
## state date fips cases
## Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1
## Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125
## California : 1154 Median :2021-09-11 Median :29.00 Median : 418120
## Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941
## Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318
## Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158
## (Other) :51184
## deaths geo_id population pop_density
## Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292
## 1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659
## Median : 5901 Median :29.00 Median : 4468402 Median : 107.860
## Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031
## 3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511
## Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120
## NA's :1106
## abb
## WA : 1158
## IL : 1155
## CA : 1154
## AZ : 1153
## MA : 1147
## WI : 1143
## (Other):51184
min(cv_states$date)
## [1] "2020-01-21"
max(cv_states$date)
## [1] "2023-03-23"
new_cases and new_deaths and
correct outliersAdd variables for new cases, new_cases, and new
deaths, new_deaths:
new_cases equal to the difference
between cases on date i and date i-1, starting on date i=2Filter to dates after June 1, 2022
Use plotly for EDA: See if there are outliers or
values that don’t make sense for new_cases and
new_deaths. Which states and which dates have strange
values?
Correct outliers: Set negative values for new_cases
or new_deaths to 0
Recalculate cases and deaths as
cumulative sum of updated new_cases and
new_deaths
Get the rolling average of new cases and new deaths to smooth over time
Inspect data again interactively
# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
cv_subset = subset(cv_states, state == state_list[i])
cv_subset = cv_subset[order(cv_subset$date),]
# add starting level for new cases and deaths
cv_subset$new_cases = cv_subset$cases[1]
cv_subset$new_deaths = cv_subset$deaths[1]
### FINISH THE CODE HERE
for (j in 2:nrow(cv_subset)) {
cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1]
cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
}
# include in main dataset
cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
# Focus on recent dates
cv_states <- cv_states %>% dplyr::filter(date >= "2022-06-01")
### FINISH THE CODE HERE
# Inspect outliers in new_cases using plotly
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)
p1<-NULL # to clear from workspace
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
p2<-NULL # to clear from workspace
# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
cv_subset = subset(cv_states, state == state_list[i])
# add starting level for new cases and deaths
cv_subset$cases = cv_subset$cases[1]
cv_subset$deaths = cv_subset$deaths[1]
### FINISH CODE HERE
for (j in 2:nrow(cv_subset)) {
cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
}
# include in main dataset
cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}
# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)
# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
p2=NULL
Add population-normalized (by 100,000) variables for each
variable type (rounded to 1 decimal place). Make sure the variables you
calculate are in the correct format (numeric). You can use
the following variable names:
per100k = cases per 100,000 populationnewper100k= new cases per 100,000deathsper100k = deaths per 100,000newdeathsper100k = new deaths per 100,000Add a “naive CFR” variable representing
deaths / cases on each date for each state
Create a dataframe representing values on the most recent date,
cv_states_today, as done in lecture
### FINISH CODE HERE
# add population normalized (by 100,000) counts for each variable
cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))
# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))
# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))
plot_ly()plot_ly() representing
pop_density vs. various variables (e.g. cases,
per100k, deaths, deathsper100k)
for each state on most recent date (cv_states_today)
hovermode = "compare"### FINISH CODE HERE
# pop_density vs. cases
cv_states_today %>%
plot_ly(x = ~pop_density, y = ~cases,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# filter out "District of Columbia"
cv_states_today_filter <- cv_states_today %>% filter(state!="District of Columbia")
# pop_density vs. cases after filtering
cv_states_today_filter %>%
plot_ly(x = ~pop_density, y = ~cases,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# pop_density vs. deathsper100k
cv_states_today_filter %>%
plot_ly(x = ~pop_density, y = ~deathsper100k,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# Adding hoverinfo
cv_states_today_filter %>%
plot_ly(x = ~pop_density, y = ~deathsper100k,
type = 'scatter', mode = 'markers', color = ~state,
size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
hoverinfo = 'text',
text = ~paste( paste(state, ":", sep=""),
paste(" Cases per 100k: ", per100k, sep="") ,
paste(" Deaths per 100k: ", deathsper100k, sep=""), sep = "<br>")) %>%
layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
yaxis = list(title = "Deaths per 100k"),
xaxis = list(title = "Population Density"),
hovermode = "compare")
ggplotly() and geom_smooth()pop_density vs. newdeathsper100k
create a chart with the same variables using
gglot_ly()geom_smooth()
pop_density is a
correlate of newdeathsper100k?### FINISH CODE HERE
p <- ggplot(cv_states_today_filter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth()
ggplotly(p)
naive_CFR for all states
over time using plot_ly()
naive_CFR for
the states that had an increase in September. How have they changed over
time?new_cases and new_deaths together in one plot.
Hint: use add_layer()
### FINISH CODE HERE
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")
### FINISH CODE HERE
# Line chart for Florida showing new_cases and new_deaths together
cv_states %>% filter(state=="Florida") %>% plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>%
add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines")
Create a heatmap to visualize new_cases for each state
on each date greater than June 1st, 2021 - Start by mapping selected
features in the dataframe into a matrix using the tidyr
package function pivot_wider(), naming the rows and
columns, as done in the lecture notes - Use plot_ly() to
create a heatmap out of this matrix. Which states stand out? - Repeat
with newper100k variable. Now which states stand out? -
Create a second heatmap in which the pattern of new_cases
for each state over time becomes more clear by filtering to only look at
dates every two weeks
### FINISH CODE HERE
# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% dplyr::filter(date>as.Date("2022-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)
# Repeat with newper100k
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% dplyr::filter(date>as.Date("2022-06-15"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)
# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2022-06-15"), as.Date("2022-11-01"), by="2 weeks")
cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
z=~cv_states_mat2,
type="heatmap",
showscale=T)
naive_CFR by state on
October 15, 2021naive_CFR by state
on most recent datesubplot(). Make sure
the shading is for the same range of values (google is your friend for
this)### For specified date
pick.date = "2022-10-15"
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states %>%
filter(date==pick.date) %>%
select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL
# Create hover text
cv_per100$hover <- with(cv_per100,
paste(state_name, '<br>',
"Cases per 100k: ", newper100k, '<br>',
"Cases: ", cases, '<br>',
"Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
# Make sure both maps are on the same color scale
shadeLimit <- 35
# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>%
add_trace(
z = ~newper100k,
text = ~hover,
locations = ~state,
color = ~newper100k,
colors = 'Purples'
)
fig <- fig %>%
colorbar(title = "Cases per 100k", limits = c(0,shadeLimit))
fig <- fig %>%
layout(
geo = set_map_details
)
fig_pick.date <- fig
#############
### Map for today's date
# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today %>%
select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL
# Create hover text
cv_per100$hover <- with(cv_per100,
paste(state_name, '<br>',
"Cases per 100k: ", newper100k, '<br>',
"Cases: ", cases, '<br>',
"Deaths: ", deaths))
# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>%
add_trace(
z = ~newper100k,
text = ~hover,
locations = ~state,
color = ~newper100k,
colors = 'Purples'
)
fig <- fig %>%
colorbar(title = "Cases per 100k", limits = c(0,shadeLimit))
fig <- fig %>%
layout(
geo = set_map_details
)
fig_Today <- fig
### Plot together
subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)